The effect of in-vehicle automation and reliability on driver situation awareness and trust

The objective of this study was to define a new transactional model of situation awareness (SA) in various driving behaviors and activities, as influenced by automation and in-vehicle device use. Twenty participants drove a virtual car and performed a freeway driving task (Experiment A) as well as a suburb navigation task (Experiment B). In the freeway driving, participants were required to drive using adaptive cruise control (ACC) or manual control modes, and received navigation information from one of two sources: a human or in-vehicle automation aid via cell phone or separate display screen, respectively. During the navigation driving, participants were required to drive through the suburban area following all traffic signs and directions from the navigation aid under different levels of information reliability (100%, 80% and 60%). A control condition was also used in which aids only presented a telemarketing survey and participants navigated using a map. Driver SA, workload, trust and multiple dimensions of task performance were collected during the experiments.; ANOVA results indicated use of the ACC system to improve driver SA and operational driver behaviors by reducing the task load in Experiment A. ANOVA results for Experiment B revealed that perfect navigation information generally improved driving performance and driver SA for strategic driving behavior compared to unreliable navigation aid information and the control condition (task-irrelevant information). The results also revealed that drivers had higher initial trust expectations and expectation of fewer errors by the automation compared to the human. However, when participants experienced automation aid errors or inefficiency, their trust in the automation declined more sharply than trust in the human advisor. The results of this empirical work provide insight into the importance of driver SA in operational and strategic type driving tasks and associated actions. It identifies in-vehicle automation and devices as underlying factors in linkages of levels of SA to specific driving behaviors in the transactional model and serves to quantify the impact of the factors on driving performance. Validation of the proposed model and identification of other underlying factors may lead to its future use for predictive purposes.